Overview

Dataset statistics

Number of variables11
Number of observations552174
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.3 MiB
Average record size in memory88.0 B

Variable types

Numeric11

Alerts

fact_latitude is highly overall correlated with gfs_a_vorticityHigh correlation
gfs_a_vorticity is highly overall correlated with fact_latitudeHigh correlation
gfs_clouds_sea is highly skewed (γ1 = 63.01067827)Skewed
df_index has unique valuesUnique
cmc_precipitations has 304817 (55.2%) zerosZeros
gfs_cloudness has 93193 (16.9%) zerosZeros
gfs_clouds_sea has 459498 (83.2%) zerosZeros

Reproduction

Analysis started2023-05-04 13:52:14.843351
Analysis finished2023-05-04 13:53:17.217873
Duration1 minute and 2.37 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

df_index
Real number (ℝ)

Distinct552174
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2282225.6
Minimum1993574
Maximum2571812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2023-05-04T15:53:17.405874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1993574
5-th percentile2022152.6
Q12137952.2
median2282281.5
Q32426826.8
95-th percentile2542724.4
Maximum2571812
Range578238
Interquartile range (IQR)288874.5

Descriptive statistics

Standard deviation166937.39
Coefficient of variation (CV)0.073146752
Kurtosis-1.1996521
Mean2282225.6
Median Absolute Deviation (MAD)144438.5
Skewness0.0012994479
Sum1.2601857 × 1012
Variance2.7868093 × 1010
MonotonicityStrictly increasing
2023-05-04T15:53:17.658879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1993574 1
 
< 0.1%
2378194 1
 
< 0.1%
2378211 1
 
< 0.1%
2378209 1
 
< 0.1%
2378208 1
 
< 0.1%
2378207 1
 
< 0.1%
2378206 1
 
< 0.1%
2378205 1
 
< 0.1%
2378204 1
 
< 0.1%
2378203 1
 
< 0.1%
Other values (552164) 552164
> 99.9%
ValueCountFrequency (%)
1993574 1
< 0.1%
1993575 1
< 0.1%
1993576 1
< 0.1%
1993577 1
< 0.1%
1993578 1
< 0.1%
1993579 1
< 0.1%
1993580 1
< 0.1%
1993581 1
< 0.1%
1993582 1
< 0.1%
1993583 1
< 0.1%
ValueCountFrequency (%)
2571812 1
< 0.1%
2571811 1
< 0.1%
2571810 1
< 0.1%
2571809 1
< 0.1%
2571808 1
< 0.1%
2571806 1
< 0.1%
2571805 1
< 0.1%
2571804 1
< 0.1%
2571803 1
< 0.1%
2571802 1
< 0.1%

fact_time
Real number (ℝ)

Distinct66528
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5436077 × 109
Minimum1.53576 × 109
Maximum1.5513947 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2023-05-04T15:53:17.890874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.53576 × 109
5-th percentile1.5365322 × 109
Q11.5395616 × 109
median1.5438276 × 109
Q31.5476421 × 109
95-th percentile1.5506418 × 109
Maximum1.5513947 × 109
Range15634680
Interquartile range (IQR)8080500

Descriptive statistics

Standard deviation4586701.6
Coefficient of variation (CV)0.0029714167
Kurtosis-1.2548048
Mean1.5436077 × 109
Median Absolute Deviation (MAD)4035600
Skewness-0.01290111
Sum8.5234002 × 1014
Variance2.1037831 × 1013
MonotonicityNot monotonic
2023-05-04T15:53:18.424870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1546354800 285
 
0.1%
1546322400 234
 
< 0.1%
1549454400 230
 
< 0.1%
1537272000 226
 
< 0.1%
1550923200 221
 
< 0.1%
1536840000 220
 
< 0.1%
1539000000 220
 
< 0.1%
1546236000 219
 
< 0.1%
1537876800 210
 
< 0.1%
1546376400 210
 
< 0.1%
Other values (66518) 549899
99.6%
ValueCountFrequency (%)
1535760000 141
< 0.1%
1535760120 1
 
< 0.1%
1535760180 1
 
< 0.1%
1535760540 1
 
< 0.1%
1535760900 6
 
< 0.1%
1535761140 1
 
< 0.1%
1535761200 4
 
< 0.1%
1535761800 8
 
< 0.1%
1535762100 3
 
< 0.1%
1535762280 1
 
< 0.1%
ValueCountFrequency (%)
1551394680 1
 
< 0.1%
1551394560 1
 
< 0.1%
1551394380 4
< 0.1%
1551394260 1
 
< 0.1%
1551394200 5
< 0.1%
1551394020 1
 
< 0.1%
1551393660 1
 
< 0.1%
1551393600 2
 
< 0.1%
1551393300 8
< 0.1%
1551393120 1
 
< 0.1%

fact_latitude
Real number (ℝ)

Distinct6137
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.667039
Minimum-53.8
Maximum69.604167
Zeros0
Zeros (%)0.0%
Negative130582
Negative (%)23.6%
Memory size4.2 MiB
2023-05-04T15:53:18.623732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-53.8
5-th percentile-22.0184
Q11.35019
median14.7944
Q334.176139
95-th percentile47.493099
Maximum69.604167
Range123.40417
Interquartile range (IQR)32.825949

Descriptive statistics

Standard deviation21.181508
Coefficient of variation (CV)1.351979
Kurtosis-0.58041734
Mean15.667039
Median Absolute Deviation (MAD)17.284018
Skewness-0.21950555
Sum8650931.7
Variance448.65626
MonotonicityNot monotonic
2023-05-04T15:53:18.798536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12.4147 1107
 
0.2%
19.088699 1080
 
0.2%
22.950399 1057
 
0.2%
-2.424722 1038
 
0.2%
12.6799 1038
 
0.2%
16.907301 1035
 
0.2%
2.841389 1028
 
0.2%
8.48212 1026
 
0.2%
-16.885799 1023
 
0.2%
19.969801 1015
 
0.2%
Other values (6127) 541727
98.1%
ValueCountFrequency (%)
-53.8 13
 
< 0.1%
-53.7777 126
< 0.1%
-53.2537 12
 
< 0.1%
-51.8228 26
 
< 0.1%
-51.82 25
 
< 0.1%
-51.685699 2
 
< 0.1%
-51.671501 49
 
< 0.1%
-51.616667 19
 
< 0.1%
-51.6089 98
< 0.1%
-50.2803 1
 
< 0.1%
ValueCountFrequency (%)
69.604167 15
 
< 0.1%
69.6 45
 
< 0.1%
69.433296 72
< 0.1%
66.600098 50
< 0.1%
66.571503 84
< 0.1%
66.249603 55
< 0.1%
65.697899 36
 
< 0.1%
65.240402 36
 
< 0.1%
64.727203 76
< 0.1%
63.985001 123
< 0.1%

fact_longitude
Real number (ℝ)

Distinct8577
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8239112
Minimum-166.339
Maximum174.80499
Zeros126
Zeros (%)< 0.1%
Negative266700
Negative (%)48.3%
Memory size4.2 MiB
2023-05-04T15:53:18.978472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-166.339
5-th percentile-105.867
Q1-76.373199
median2.73881
Q391.813301
95-th percentile124.56667
Maximum174.80499
Range341.144
Interquartile range (IQR)168.1865

Descriptive statistics

Standard deviation81.877453
Coefficient of variation (CV)21.411965
Kurtosis-1.3899749
Mean3.8239112
Median Absolute Deviation (MAD)79.605807
Skewness0.13059339
Sum2111464.3
Variance6703.9173
MonotonicityNot monotonic
2023-05-04T15:53:19.150543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130.876999 1107
 
0.2%
72.867897 1080
 
0.2%
120.206001 1057
 
0.2%
-54.785831 1038
 
0.2%
101.004997 1038
 
0.2%
-60.692223 1028
 
0.2%
76.920097 1026
 
0.2%
-75.835403 1015
 
0.2%
-38.322498 993
 
0.2%
100.747002 984
 
0.2%
Other values (8567) 541808
98.1%
ValueCountFrequency (%)
-166.339005 36
 
< 0.1%
-166.089005 55
< 0.1%
-165.607567 128
< 0.1%
-163.682007 91
< 0.1%
-163.302002 51
 
< 0.1%
-162.026001 42
 
< 0.1%
-161.319458 45
 
< 0.1%
-160.190994 10
 
< 0.1%
-159.985992 50
 
< 0.1%
-157.572006 34
 
< 0.1%
ValueCountFrequency (%)
174.804993 118
< 0.1%
174.804993 5
 
< 0.1%
172.531998 102
< 0.1%
153.129167 6
 
< 0.1%
153.117004 122
< 0.1%
153.11 68
< 0.1%
153.090278 8
 
< 0.1%
153.089167 2
 
< 0.1%
153.067001 121
< 0.1%
153.029999 26
 
< 0.1%

topography_bathymetry
Real number (ℝ)

Distinct1511
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.66866
Minimum-2126
Maximum4659
Zeros4260
Zeros (%)0.8%
Negative33700
Negative (%)6.1%
Memory size4.2 MiB
2023-05-04T15:53:19.334607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2126
5-th percentile-3
Q113
median68
Q3296
95-th percentile1186
Maximum4659
Range6785
Interquartile range (IQR)283

Descriptive statistics

Standard deviation443.52091
Coefficient of variation (CV)1.7484261
Kurtosis8.5979316
Mean253.66866
Median Absolute Deviation (MAD)64
Skewness2.4169882
Sum1.4006924 × 108
Variance196710.8
MonotonicityNot monotonic
2023-05-04T15:53:19.519882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 12365
 
2.2%
4 10848
 
2.0%
5 10817
 
2.0%
13 10484
 
1.9%
10 10318
 
1.9%
8 9521
 
1.7%
7 9295
 
1.7%
9 9158
 
1.7%
17 7675
 
1.4%
18 7161
 
1.3%
Other values (1501) 454532
82.3%
ValueCountFrequency (%)
-2126 37
 
< 0.1%
-1933 9
 
< 0.1%
-1930 72
 
< 0.1%
-1753 24
 
< 0.1%
-1549 250
< 0.1%
-1489 78
 
< 0.1%
-1425 54
 
< 0.1%
-1055 73
 
< 0.1%
-1016 52
 
< 0.1%
-970 86
 
< 0.1%
ValueCountFrequency (%)
4659 15
 
< 0.1%
4111 11
 
< 0.1%
4048 84
< 0.1%
3965 37
< 0.1%
3832 41
< 0.1%
3717 39
< 0.1%
3655 13
 
< 0.1%
3579 49
< 0.1%
3446 28
 
< 0.1%
3436 3
 
< 0.1%

sun_elevation
Real number (ℝ)

Distinct515510
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7354199
Minimum-89.931034
Maximum89.752937
Zeros0
Zeros (%)0.0%
Negative252988
Negative (%)45.8%
Memory size4.2 MiB
2023-05-04T15:53:19.725491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-89.931034
5-th percentile-68.536215
Q1-33.473796
median5.9104228
Q335.35573
95-th percentile65.860191
Maximum89.752937
Range179.68397
Interquartile range (IQR)68.829527

Descriptive statistics

Standard deviation42.218577
Coefficient of variation (CV)24.327586
Kurtosis-0.99794841
Mean1.7354199
Median Absolute Deviation (MAD)33.802301
Skewness-0.14993204
Sum958253.74
Variance1782.4083
MonotonicityNot monotonic
2023-05-04T15:53:19.900132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-24.58489175 6
 
< 0.1%
43.03386155 5
 
< 0.1%
-14.37241944 5
 
< 0.1%
69.89324332 5
 
< 0.1%
-5.423531709 5
 
< 0.1%
21.27891593 4
 
< 0.1%
55.76084945 4
 
< 0.1%
31.8274272 4
 
< 0.1%
60.28850156 4
 
< 0.1%
11.83557023 4
 
< 0.1%
Other values (515500) 552128
> 99.9%
ValueCountFrequency (%)
-89.93103362 1
< 0.1%
-89.84408446 1
< 0.1%
-89.80431511 1
< 0.1%
-89.78064168 1
< 0.1%
-89.72209825 1
< 0.1%
-89.68493111 1
< 0.1%
-89.63013349 1
< 0.1%
-89.5875716 1
< 0.1%
-89.58346809 1
< 0.1%
-89.55879368 1
< 0.1%
ValueCountFrequency (%)
89.75293669 1
< 0.1%
89.7121502 2
< 0.1%
89.70293561 2
< 0.1%
89.66369858 1
< 0.1%
89.56848526 2
< 0.1%
89.53481762 1
< 0.1%
89.52475207 1
< 0.1%
89.4995362 1
< 0.1%
89.4972407 1
< 0.1%
89.49242245 1
< 0.1%

cmc_precipitations
Real number (ℝ)

Distinct126332
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16751069
Minimum-3.3333333 × 10-5
Maximum26.447317
Zeros304817
Zeros (%)55.2%
Negative7327
Negative (%)1.3%
Memory size4.2 MiB
2023-05-04T15:53:20.093647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-3.3333333 × 10-5
5-th percentile0
Q10
median0
Q30.02235
95-th percentile1.037995
Maximum26.447317
Range26.44735
Interquartile range (IQR)0.02235

Descriptive statistics

Standard deviation0.59954601
Coefficient of variation (CV)3.5791509
Kurtosis101.95089
Mean0.16751069
Median Absolute Deviation (MAD)0
Skewness7.719795
Sum92495.045
Variance0.35945542
MonotonicityNot monotonic
2023-05-04T15:53:20.270129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 304817
55.2%
8.333333333 × 10-61251
 
0.2%
-8.333333333 × 10-61128
 
0.2%
1.666666667 × 10-51049
 
0.2%
8.333333334 × 10-6858
 
0.2%
8.333333333 × 10-6705
 
0.1%
3.333333333 × 10-5664
 
0.1%
-8.333333333 × 10-6656
 
0.1%
1.666666667 × 10-5532
 
0.1%
-8.333333334 × 10-6515
 
0.1%
Other values (126322) 239999
43.5%
ValueCountFrequency (%)
-3.333333333 × 10-51
 
< 0.1%
-3.333333333 × 10-51
 
< 0.1%
-3.333333333 × 10-51
 
< 0.1%
-3.333333333 × 10-51
 
< 0.1%
-3.333333333 × 10-51
 
< 0.1%
-3.333333333 × 10-56
 
< 0.1%
-3.333333333 × 10-511
 
< 0.1%
-3.333333333 × 10-51
 
< 0.1%
-1.666666667 × 10-5101
< 0.1%
-1.666666667 × 10-516
 
< 0.1%
ValueCountFrequency (%)
26.44731667 1
< 0.1%
20.94596667 1
< 0.1%
19.59825833 1
< 0.1%
18.75138333 1
< 0.1%
18.33026667 1
< 0.1%
18.07283333 1
< 0.1%
18.00921667 1
< 0.1%
17.74750833 1
< 0.1%
17.359375 1
< 0.1%
16.24133333 1
< 0.1%

gfs_a_vorticity
Real number (ℝ)

Distinct469242
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5650346 × 10-5
Minimum-0.00063446508
Maximum0.0020388791
Zeros0
Zeros (%)0.0%
Negative168341
Negative (%)30.5%
Memory size4.2 MiB
2023-05-04T15:53:20.449244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.00063446508
5-th percentile-9.908194 × 10-5
Q1-1.2583816 × 10-5
median3.8585693 × 10-5
Q38.3628664 × 10-5
95-th percentile0.0001653836
Maximum0.0020388791
Range0.0026733442
Interquartile range (IQR)9.621248 × 10-5

Descriptive statistics

Standard deviation8.6170191 × 10-5
Coefficient of variation (CV)2.4170927
Kurtosis4.1863261
Mean3.5650346 × 10-5
Median Absolute Deviation (MAD)4.7976257 × 10-5
Skewness-0.16128979
Sum19.685194
Variance7.4253018 × 10-9
MonotonicityNot monotonic
2023-05-04T15:53:20.652275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.594192458 × 10-510
 
< 0.1%
1.218475336 × 10-510
 
< 0.1%
2.948852489 × 10-59
 
< 0.1%
5.325469829 × 10-59
 
< 0.1%
4.326904309 × 10-58
 
< 0.1%
5.035400363 × 10-78
 
< 0.1%
8.829650324 × 10-58
 
< 0.1%
7.083739911 × 10-58
 
< 0.1%
0.0001026385507 7
 
< 0.1%
6.639343337 × 10-57
 
< 0.1%
Other values (469232) 552090
> 99.9%
ValueCountFrequency (%)
-0.0006344650756 1
< 0.1%
-0.0006244699471 1
< 0.1%
-0.0006177345058 1
< 0.1%
-0.0006136846496 1
< 0.1%
-0.0006096997531 1
< 0.1%
-0.000607397873 1
< 0.1%
-0.0005968618789 1
< 0.1%
-0.0005945274024 1
< 0.1%
-0.0005924057332 1
< 0.1%
-0.000591892458 1
< 0.1%
ValueCountFrequency (%)
0.002038879087 1
< 0.1%
0.001182437525 1
< 0.1%
0.0008671264513 1
< 0.1%
0.0008607942145 1
< 0.1%
0.0008515818045 1
< 0.1%
0.0008282407653 1
< 0.1%
0.0008211351233 1
< 0.1%
0.0008144315216 1
< 0.1%
0.0007711995859 1
< 0.1%
0.0007514400641 1
< 0.1%

gfs_cloudness
Real number (ℝ)

Distinct302
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75990888
Minimum0
Maximum3
Zeros93193
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2023-05-04T15:53:20.864244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.12
median0.72
Q31.04
95-th percentile2.03
Maximum3
Range3
Interquartile range (IQR)0.92

Descriptive statistics

Standard deviation0.67901258
Coefficient of variation (CV)0.89354473
Kurtosis0.60093939
Mean0.75990888
Median Absolute Deviation (MAD)0.47
Skewness0.91725319
Sum419601.93
Variance0.46105808
MonotonicityNot monotonic
2023-05-04T15:53:21.054244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93193
 
16.9%
1 38545
 
7.0%
0.01 10194
 
1.8%
1.01 9160
 
1.7%
0.99 7039
 
1.3%
0.02 6204
 
1.1%
1.02 5440
 
1.0%
0.98 5046
 
0.9%
0.03 4744
 
0.9%
0.97 4339
 
0.8%
Other values (292) 368270
66.7%
ValueCountFrequency (%)
0 93193
16.9%
0.01 10194
 
1.8%
0.02 6204
 
1.1%
0.03 4744
 
0.9%
0.04 3933
 
0.7%
0.05 3460
 
0.6%
0.06 3050
 
0.6%
0.07 2985
 
0.5%
0.08 2686
 
0.5%
0.09 2574
 
0.5%
ValueCountFrequency (%)
3 1875
0.3%
2.99 537
 
0.1%
2.98 407
 
0.1%
2.97 314
 
0.1%
2.96 288
 
0.1%
2.95 267
 
< 0.1%
2.94 255
 
< 0.1%
2.93 264
 
< 0.1%
2.92 221
 
< 0.1%
2.91 221
 
< 0.1%

gfs_clouds_sea
Real number (ℝ)

SKEWED  ZEROS 

Distinct511
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5858865 × 10-7
Minimum0
Maximum0.00031860001
Zeros459498
Zeros (%)83.2%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2023-05-04T15:53:21.245244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5 × 10-7
Maximum0.00031860001
Range0.00031860001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.968901 × 10-6
Coefficient of variation (CV)12.415145
Kurtosis6100.1709
Mean1.5858865 × 10-7
Median Absolute Deviation (MAD)0
Skewness63.010678
Sum0.087568528
Variance3.8765713 × 10-12
MonotonicityNot monotonic
2023-05-04T15:53:21.418881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 459498
83.2%
1.000000012 × 10-738146
 
6.9%
2.000000023 × 10-713396
 
2.4%
3.000000106 × 10-77451
 
1.3%
4.000000047 × 10-75017
 
0.9%
4.999999987 × 10-73725
 
0.7%
6.000000212 × 10-73000
 
0.5%
6.999999869 × 10-72406
 
0.4%
8.000000093 × 10-72070
 
0.4%
9.000000318 × 10-71739
 
0.3%
Other values (501) 15726
 
2.8%
ValueCountFrequency (%)
0 459498
83.2%
9.999999939 × 10-921
 
< 0.1%
1.999999988 × 10-812
 
< 0.1%
2.999999893 × 10-83
 
< 0.1%
3.999999976 × 10-83
 
< 0.1%
7.999999951 × 10-82
 
< 0.1%
9.000000034 × 10-81
 
< 0.1%
1.000000012 × 10-738146
 
6.9%
1.300000037 × 10-71
 
< 0.1%
1.485953993 × 10-72
 
< 0.1%
ValueCountFrequency (%)
0.0003186000104 1
< 0.1%
0.0002824999974 1
< 0.1%
0.000269000011 1
< 0.1%
0.0002562000009 1
< 0.1%
0.0002401000093 1
< 0.1%
0.0002128000051 1
< 0.1%
0.0002023999987 1
< 0.1%
0.0001993000042 1
< 0.1%
0.0001979000081 1
< 0.1%
0.0001935000037 1
< 0.1%

gfs_humidity
Real number (ℝ)

Distinct86421
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.827107
Minimum2.6000001
Maximum100.04517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2023-05-04T15:53:21.614626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.6000001
5-th percentile26.617964
Q155.565871
median72.101707
Q383.00423
95-th percentile95.400002
Maximum100.04517
Range97.445166
Interquartile range (IQR)27.438359

Descriptive statistics

Standard deviation20.669914
Coefficient of variation (CV)0.30474414
Kurtosis0.039988186
Mean67.827107
Median Absolute Deviation (MAD)13.101707
Skewness-0.77714492
Sum37452365
Variance427.24533
MonotonicityNot monotonic
2023-05-04T15:53:21.788924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.30000305 1213
 
0.2%
77.5 1178
 
0.2%
77.09999847 1170
 
0.2%
78.59999847 1168
 
0.2%
75.80000305 1164
 
0.2%
78.5 1161
 
0.2%
78.80000305 1161
 
0.2%
75.09999847 1155
 
0.2%
76.80000305 1154
 
0.2%
79 1154
 
0.2%
Other values (86411) 540496
97.9%
ValueCountFrequency (%)
2.600000143 2
< 0.1%
2.839907169 1
 
< 0.1%
2.904468298 1
 
< 0.1%
3 3
< 0.1%
3.083759069 1
 
< 0.1%
3.100000143 2
< 0.1%
3.105132341 1
 
< 0.1%
3.200000048 3
< 0.1%
3.299999952 3
< 0.1%
3.400000095 1
 
< 0.1%
ValueCountFrequency (%)
100.045166 1
< 0.1%
100.0431213 1
< 0.1%
100.0419922 1
< 0.1%
100.0374374 1
< 0.1%
100.0337143 1
< 0.1%
100.0273895 1
< 0.1%
100.0266647 1
< 0.1%
100.0265274 1
< 0.1%
100.0241852 1
< 0.1%
100.0232086 1
< 0.1%

Interactions

2023-05-04T15:53:11.398542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:28.622780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:32.963575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:37.380565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:42.312702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:46.984043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:51.123059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:55.262546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:59.206037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:03.319518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:07.497708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:11.753755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:28.995255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:33.362740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:37.753875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:42.723915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:47.363688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:51.509591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:55.607632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:59.589572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:03.688411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:07.842748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:12.119317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:29.399698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:33.773581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:38.150100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:43.121820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:47.743673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:51.901631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:55.964790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:59.950807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:04.083550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:08.213621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:12.493693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:29.869696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:34.171658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:38.534629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:43.493856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:48.110464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:52.287042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:56.325415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:00.327914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:04.518646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:08.571480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:12.876842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:30.291476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:34.582249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:38.917593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:43.918845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:48.475257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:52.663383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:56.682543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:00.684854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:04.897024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:08.920683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:13.270320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:30.698418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:34.997598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:39.299741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:44.438846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:48.838462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:53.029962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:57.037599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:01.060342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:05.277946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:09.292794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:13.646385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:31.094499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:35.401628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:39.686090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:45.035365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:49.262096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:53.411646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:57.403116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:01.454218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:05.650885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:09.645374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:14.010735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:31.466301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:35.805462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:40.071488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:45.516363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:49.632238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:53.783855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:57.756168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:01.826255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:06.020735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:09.993068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:14.391804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:31.838003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:36.221998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:40.465827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:45.885644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:50.012767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:54.162904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:58.118395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:02.205724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:06.411601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:10.349823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:14.747648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:32.206583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:36.624059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:40.848274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:46.251756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:50.388248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:54.524947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:58.490558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:02.563892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:06.780695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:10.687534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:15.122614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:32.589477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:37.015472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:41.245352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:46.630613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:50.758519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:54.911922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:52:58.849085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:02.943994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:07.173134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-04T15:53:11.036696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-05-04T15:53:21.946870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
df_indexfact_timefact_latitudefact_longitudetopography_bathymetrysun_elevationcmc_precipitationsgfs_a_vorticitygfs_cloudnessgfs_clouds_seagfs_humidity
df_index1.0000.000-0.0370.018-0.0100.0090.002-0.0230.0060.0080.005
fact_time0.0001.0000.0040.000-0.000-0.042-0.016-0.003-0.0320.002-0.002
fact_latitude-0.0370.0041.000-0.3020.066-0.203-0.0340.689-0.139-0.062-0.036
fact_longitude0.0180.000-0.3021.000-0.2450.033-0.014-0.2140.0570.112-0.011
topography_bathymetry-0.010-0.0000.066-0.2451.0000.012-0.0260.042-0.025-0.305-0.199
sun_elevation0.009-0.042-0.2030.0330.0121.0000.024-0.1460.020-0.143-0.286
cmc_precipitations0.002-0.016-0.034-0.014-0.0260.0241.000-0.0300.3850.1100.344
gfs_a_vorticity-0.023-0.0030.689-0.2140.042-0.146-0.0301.000-0.096-0.038-0.028
gfs_cloudness0.006-0.032-0.1390.057-0.0250.0200.385-0.0961.0000.0890.379
gfs_clouds_sea0.0080.002-0.0620.112-0.305-0.1430.110-0.0380.0891.0000.225
gfs_humidity0.005-0.002-0.036-0.011-0.199-0.2860.344-0.0280.3790.2251.000

Missing values

2023-05-04T15:53:15.351521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-04T15:53:15.841895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

df_indexfact_timefact_latitudefact_longitudetopography_bathymetrysun_elevationcmc_precipitationsgfs_a_vorticitygfs_cloudnessgfs_clouds_seagfs_humidity
019935741.547967e+0933.466499-82.039398129.0-74.6048210.5219501.239536e-042.140.000000e+0088.400002
119935751.550765e+09-3.552530-80.38140126.068.6881720.0000082.977295e-071.010.000000e+0064.599998
219935761.537093e+0959.878899-1.2955602.026.3805510.6218502.820175e-042.210.000000e+0087.900002
319935771.548846e+0939.048801-84.667801263.0-29.7998330.0553751.010588e-040.060.000000e+0036.686890
419935781.547161e+0932.566700-117.1169976.027.2946870.0000009.037872e-050.000.000000e+0081.145103
519935791.547464e+0927.913000-97.2115025.0-28.6656780.0000006.861566e-050.001.000000e-0771.103088
619935801.550617e+0938.138599-78.452904175.07.2712320.0000006.575049e-051.970.000000e+0048.558178
719935811.541641e+0920.937000-89.65770012.0-20.1217870.0000002.428857e-050.490.000000e+0062.799999
819935821.541720e+0949.173302-0.45000061.0-56.1283760.0000001.357539e-040.720.000000e+0088.700005
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